No abstract

Background:Peripheral nerves at the elbow region are prone to entrapment neuropathies and injuries. To make accurate assessment, clinicians need stereoscopic display of the nerves to observe them at all angles.Objectives:To obtain a stereoscopic display of the peripheral nerves at the elbow region based on magnetic resonance (MR) diffusion tensor imaging (DTI) data using three post-processing methods of volume rendering (VR), maximum intensity projection (MIP), and fiber tractography, and to evaluate the difference and correlation between them.Subjects and Methods:Twenty-four elbows of 12 healthy young volunteers were assessed by 20 encoding diffusion direction MR DTI scans. Images belonging to a single direction (anterior-posterior direction, perpendicular to the nerve) were subjected to VR and MIP reconstruction. All raw DTI data were transferred to the Siemens MR workstation for fiber tractography post-processing. Imaging qualities of fiber tractography and VR/MIP were evaluated by two observers independently based on a custom evaluation scale.Results:Stereoscopic displays of the nerves were obtained in all 24 elbows by VR, MIP, and fiber tractography post-processing methods. The VR/MIP post-processing methods were easier to perform compared to fiber tractography. There was no significant difference among the scores of fiber tracking and VR/MIP reconstruction for single direction. The imaging quality scores of fiber tractography and VR/MIP were significantly correlated based on intraclass correlation coefficient (ICC) analysis (ICC ranged 0.709 - 0.901), which suggested that the scores based on fiber tractography and VR/MIP for the same sample were consistent. Inter- and intraobserver agreements were good to excellent.Conclusion:Stereoscopic displays of the peripheral nerves at the elbow region can be achieved by using VR, MIP, and fiber tracking post-processing methods based on raw DTI images. VR and MIP reconstruction could be used as preview tools before fiber tracking to determine whether the raw images are satisfactory.

Background: In addition to fiber tracking, stereoscopic display of the peripheral nerves can be obtained based on magnetic resonance (MR) diffusion tensor imaging (DTI) data using post-processing methods, including volume rendering (VR) and maximum intensity projection (MIP). However, sufficient suppression of the image noise remains a challenge. Objectives: To achieve three-dimensional (3D) display of the peripheral nerves in the wrist region using two post-processing methods for DTI, i.e. VR reconstruction for single-direction images and the subtraction of unidirectionally encoded images for suppression of heavily isotropic objects (SUSHI); to compare the quality of images obtained via the two approaches; and to explore their clinical applications. Materials and Methods: We performed DTI scans using 6 (DTI6) and 25 (DTI25) encoding diffusion directions for 20 wrists of 10 healthy adult volunteers. We used VR to reconstruct 2 types of images: 1, single-direction (anterior-posterior [AP] direction) and 2, SUSHI (AP direction with the subtraction of the superior-inferior [SI] direction). The 3D nerve image quality, noise level, and degree of noise-removal difficulty were evaluated according to custom evaluation scales. The preliminary clinical applications of these methods were explored through follow-ups with patients with nerve laceration in the wrist region. Results: Single-direction VR reconstruction clearly showed the nerves for both DTI6 and DTI25 but with obvious noise. In DTI25, VR reconstruction for SUSHI showed the nerves clearly with excellent nerve signal intensity. In DTI6, SUSHI post-processing lost some ulnar nerve signal intensity, resulting in a significant difference in image quality scores between single-direction images and SUSHI. Most of the noise was removed after SUSHI post-processing. Conclusion: VR reconstruction for both single-direction images and SUSHI using DTI25 raw data provides excellent 3D displays of the peripheral nerves in the wrist region. SUSHI post-processing is a useful denoising tool because it automatically reduces the majority of isotropic object noise.